update with main (#1152)

* v0.8.7 * update cmd version * set openfst HAVE_BIN/HAVE_SCRIPT off for win32 * 修复为支持新版本的热词 (#1137) * update CMakeLists.txt * Revert "update CMakeLists.txt" This reverts commit 54bcd1f674. * rm log.h for wins-websocket * fix bug of websocket lock blocking * update funasr-wss-server * update model-revision by model name * update funasr-wss-server-2pass * 增加分角色语音识别对ERes2Net模型的支持。 * Update README.md (#1140) minor fix * automatically configure parameters such as decoder-thread-num * update docs * update docs * update docs * 分角色语音识别支持更多的模型 * update spk inference * remove never use code (#1151) --------- Co-authored-by: 雾聪 <[email protected]> Co-authored-by: 夜雨飘零 <[email protected]> Co-authored-by: Ikko Eltociear Ashimine <[email protected]> Co-authored-by: Shi Xian <[email protected]> Co-authored-by: shixian.shi <[email protected]>
2026-10-12 02:19:18 +00:00 · 2023-12-06 19:54:37 +08:00
parent 1c2eb051cd
commit 81acb17544
28 changed files with 1370 additions and 519 deletions
@@ -153,7 +153,7 @@ You can also scan the following DingTalk group or WeChat group QR code to join t
 The contributors can be found in [contributors list](./Acknowledge.md)

 ## License
-This project is licensed under the [The MIT License](https://opensource.org/licenses/MIT). FunASR also contains various third-party components and some code modified from other repos under other open source licenses.
+This project is licensed under [The MIT License](https://opensource.org/licenses/MIT). FunASR also contains various third-party components and some code modified from other repos under other open source licenses.
 The use of pretraining model is subject to [model license](./MODEL_LICENSE)


@@ -48,13 +48,13 @@ from funasr.utils.types import str2bool
 from funasr.utils.types import str2triple_str
 from funasr.utils.types import str_or_none
 from funasr.utils.vad_utils import slice_padding_fbank
-from funasr.utils.speaker_utils import (check_audio_list, 
-                                        sv_preprocess, 
-                                        sv_chunk, 
-                                        CAMPPlus, 
-                                        extract_feature, 
+from funasr.utils.speaker_utils import (check_audio_list,
+                                        sv_preprocess,
+                                        sv_chunk,
+                                        extract_feature,
                                        postprocess,
                                        distribute_spk)
+import funasr.modules.cnn as sv_module
 from funasr.build_utils.build_model_from_file import build_model_from_file
 from funasr.utils.cluster_backend import ClusterBackend
 from funasr.utils.modelscope_utils import get_cache_dir
@@ -818,7 +818,15 @@ def inference_paraformer_vad_speaker(
        format="%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s",
    )

-    sv_model_file = asr_model_file.replace("model.pb", "campplus_cn_common.bin")
+    sv_model_config_path = asr_model_file.replace("model.pb", "sv_model_config.yaml")
+    if not os.path.exists(sv_model_config_path):
+        sv_model_config = {'sv_model_class': 'CAMPPlus','sv_model_file': 'campplus_cn_common.bin', 'models_config': {}}
+    else:
+        with open(sv_model_config_path, 'r') as f:
+            sv_model_config = yaml.load(f, Loader=yaml.FullLoader)
+    if sv_model_config['models_config'] is None:
+        sv_model_config['models_config'] = {}
+    sv_model_file = asr_model_file.replace("model.pb", sv_model_config['sv_model_file'])

    if param_dict is not None:
        hotword_list_or_file = param_dict.get('hotword')
@@ -944,9 +952,15 @@ def inference_paraformer_vad_speaker(
            #####  speaker_verification  #####
            ##################################
            # load sv model
-            sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
-            sv_model = CAMPPlus()
+            if ngpu > 0:
+                sv_model_dict = torch.load(sv_model_file)
+                sv_model = getattr(sv_module, sv_model_config['sv_model_class'])(**sv_model_config['models_config'])
+                sv_model.cuda()
+            else:
+                sv_model_dict = torch.load(sv_model_file, map_location=torch.device('cpu'))
+                sv_model = getattr(sv_module, sv_model_config['sv_model_class'])(**sv_model_config['models_config'])
            sv_model.load_state_dict(sv_model_dict)
+            print(f'load sv model params: {sv_model_file}')
            sv_model.eval()
            cb_model = ClusterBackend()
            vad_segments = []
@@ -969,9 +983,11 @@ def inference_paraformer_vad_speaker(
                    embs = []
                    for x in wavs:
                        x = extract_feature([x])
+                        if ngpu > 0:
+                            x = x.cuda()
                        embs.append(sv_model(x))
                    embs = torch.cat(embs)
-                    embeddings.append(embs.detach().numpy())
+                    embeddings.append(embs.cpu().detach().numpy())
                embeddings = np.concatenate(embeddings)
                labels = cb_model(embeddings)
                sv_output = postprocess(segments, vad_segments, labels, embeddings)
@@ -0,0 +1,108 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+""" This implementation is adapted from https://github.com/wenet-e2e/wespeaker."""
+
+import torch
+import torch.nn as nn
+
+
+class TAP(nn.Module):
+    """
+    Temporal average pooling, only first-order mean is considered
+    """
+
+    def __init__(self, **kwargs):
+        super(TAP, self).__init__()
+
+    def forward(self, x):
+        pooling_mean = x.mean(dim=-1)
+        # To be compatable with 2D input
+        pooling_mean = pooling_mean.flatten(start_dim=1)
+        return pooling_mean
+
+
+class TSDP(nn.Module):
+    """
+    Temporal standard deviation pooling, only second-order std is considered
+    """
+
+    def __init__(self, **kwargs):
+        super(TSDP, self).__init__()
+
+    def forward(self, x):
+        # The last dimension is the temporal axis
+        pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-8)
+        pooling_std = pooling_std.flatten(start_dim=1)
+        return pooling_std
+
+
+class TSTP(nn.Module):
+    """
+    Temporal statistics pooling, concatenate mean and std, which is used in
+    x-vector
+    Comment: simple concatenation can not make full use of both statistics
+    """
+
+    def __init__(self, **kwargs):
+        super(TSTP, self).__init__()
+
+    def forward(self, x):
+        # The last dimension is the temporal axis
+        pooling_mean = x.mean(dim=-1)
+        pooling_std = torch.sqrt(torch.var(x, dim=-1) + 1e-8)
+        pooling_mean = pooling_mean.flatten(start_dim=1)
+        pooling_std = pooling_std.flatten(start_dim=1)
+
+        stats = torch.cat((pooling_mean, pooling_std), 1)
+        return stats
+
+
+class ASTP(nn.Module):
+    """ Attentive statistics pooling: Channel- and context-dependent
+        statistics pooling, first used in ECAPA_TDNN.
+    """
+
+    def __init__(self, in_dim, bottleneck_dim=128, global_context_att=False):
+        super(ASTP, self).__init__()
+        self.global_context_att = global_context_att
+
+        # Use Conv1d with stride == 1 rather than Linear, then we don't
+        # need to transpose inputs.
+        if global_context_att:
+            self.linear1 = nn.Conv1d(
+                in_dim * 3, bottleneck_dim,
+                kernel_size=1)  # equals W and b in the paper
+        else:
+            self.linear1 = nn.Conv1d(
+                in_dim, bottleneck_dim,
+                kernel_size=1)  # equals W and b in the paper
+        self.linear2 = nn.Conv1d(bottleneck_dim, in_dim,
+                                 kernel_size=1)  # equals V and k in the paper
+
+    def forward(self, x):
+        """
+        x: a 3-dimensional tensor in tdnn-based architecture (B,F,T)
+            or a 4-dimensional tensor in resnet architecture (B,C,F,T)
+            0-dim: batch-dimension, last-dim: time-dimension (frame-dimension)
+        """
+        if len(x.shape) == 4:
+            x = x.reshape(x.shape[0], x.shape[1] * x.shape[2], x.shape[3])
+        assert len(x.shape) == 3
+
+        if self.global_context_att:
+            context_mean = torch.mean(x, dim=-1, keepdim=True).expand_as(x)
+            context_std = torch.sqrt(
+                torch.var(x, dim=-1, keepdim=True) + 1e-10).expand_as(x)
+            x_in = torch.cat((x, context_mean, context_std), dim=1)
+        else:
+            x_in = x
+
+        # DON'T use ReLU here! ReLU may be hard to converge.
+        alpha = torch.tanh(
+            self.linear1(x_in))  # alpha = F.relu(self.linear1(x_in))
+        alpha = torch.softmax(self.linear2(alpha), dim=2)
+        mean = torch.sum(alpha * x, dim=2)
+        var = torch.sum(alpha * (x ** 2), dim=2) - mean ** 2
+        std = torch.sqrt(var.clamp(min=1e-10))
+        return torch.cat([mean, std], dim=1)
@@ -0,0 +1,124 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+from collections import OrderedDict
+
+import torch.nn.functional as F
+from torch import nn
+
+from funasr.modules.cnn.layers import DenseLayer, StatsPool, TDNNLayer, CAMDenseTDNNBlock, TransitLayer, \
+    BasicResBlock, get_nonlinear
+
+
+class FCM(nn.Module):
+    def __init__(self,
+                 block=BasicResBlock,
+                 num_blocks=[2, 2],
+                 m_channels=32,
+                 feat_dim=80):
+        super(FCM, self).__init__()
+        self.in_planes = m_channels
+        self.conv1 = nn.Conv2d(1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(m_channels)
+
+        self.layer1 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
+        self.layer2 = self._make_layer(block, m_channels, num_blocks[0], stride=2)
+
+        self.conv2 = nn.Conv2d(m_channels, m_channels, kernel_size=3, stride=(2, 1), padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(m_channels)
+        self.out_channels = m_channels * (feat_dim // 8)
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = x.unsqueeze(1)
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = F.relu(self.bn2(self.conv2(out)))
+
+        shape = out.shape
+        out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
+        return out
+
+
+class CAMPPlus(nn.Module):
+    def __init__(self,
+                 feat_dim=80,
+                 embedding_size=192,
+                 growth_rate=32,
+                 bn_size=4,
+                 init_channels=128,
+                 config_str='batchnorm-relu',
+                 memory_efficient=True,
+                 output_level='segment'):
+        super(CAMPPlus, self).__init__()
+
+        self.head = FCM(feat_dim=feat_dim)
+        channels = self.head.out_channels
+        self.output_level = output_level
+
+        self.xvector = nn.Sequential(
+            OrderedDict([
+
+                ('tdnn',
+                 TDNNLayer(channels,
+                           init_channels,
+                           5,
+                           stride=2,
+                           dilation=1,
+                           padding=-1,
+                           config_str=config_str)),
+            ]))
+        channels = init_channels
+        for i, (num_layers, kernel_size,
+                dilation) in enumerate(zip((12, 24, 16), (3, 3, 3), (1, 2, 2))):
+            block = CAMDenseTDNNBlock(num_layers=num_layers,
+                                      in_channels=channels,
+                                      out_channels=growth_rate,
+                                      bn_channels=bn_size * growth_rate,
+                                      kernel_size=kernel_size,
+                                      dilation=dilation,
+                                      config_str=config_str,
+                                      memory_efficient=memory_efficient)
+            self.xvector.add_module('block%d' % (i + 1), block)
+            channels = channels + num_layers * growth_rate
+            self.xvector.add_module(
+                'transit%d' % (i + 1),
+                TransitLayer(channels,
+                             channels // 2,
+                             bias=False,
+                             config_str=config_str))
+            channels //= 2
+
+        self.xvector.add_module(
+            'out_nonlinear', get_nonlinear(config_str, channels))
+
+        if self.output_level == 'segment':
+            self.xvector.add_module('stats', StatsPool())
+            self.xvector.add_module(
+                'dense',
+                DenseLayer(
+                    channels * 2, embedding_size, config_str='batchnorm_'))
+        else:
+            assert self.output_level == 'frame', '`output_level` should be set to \'segment\' or \'frame\'. '
+
+        for m in self.modules():
+            if isinstance(m, (nn.Conv1d, nn.Linear)):
+                nn.init.kaiming_normal_(m.weight.data)
+                if m.bias is not None:
+                    nn.init.zeros_(m.bias)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+        x = self.head(x)
+        x = self.xvector(x)
+        if self.output_level == 'frame':
+            x = x.transpose(1, 2)
+        return x
@@ -0,0 +1,420 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+""" Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
+    ERes2Net incorporates both local and global feature fusion techniques to improve the performance. 
+    The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
+    The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
+    ERes2Net-Large is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better 
+    recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
+"""
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import funasr.models.pooling.pooling_layers as pooling_layers
+from funasr.modules.cnn.fusion import AFF
+
+
+class ReLU(nn.Hardtanh):
+
+    def __init__(self, inplace=False):
+        super(ReLU, self).__init__(0, 20, inplace)
+
+    def __repr__(self):
+        inplace_str = 'inplace' if self.inplace else ''
+        return self.__class__.__name__ + ' (' \
+            + inplace_str + ')'
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    "1x1 convolution without padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
+                     padding=0, bias=False)
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlockERes2Net(nn.Module):
+    expansion = 2
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
+        super(BasicBlockERes2Net, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale
+
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes,
+                          self.expansion * planes,
+                          kernel_size=1,
+                          stride=stride,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class BasicBlockERes2Net_diff_AFF(nn.Module):
+    expansion = 2
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
+        super(BasicBlockERes2Net_diff_AFF, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale
+
+        convs = []
+        fuse_models = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        for j in range(self.nums - 1):
+            fuse_models.append(AFF(channels=width))
+
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.fuse_models = nn.ModuleList(fuse_models)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes,
+                          self.expansion * planes,
+                          kernel_size=1,
+                          stride=stride,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = self.fuse_models[i - 1](sp, spx[i])
+
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ERes2Net(nn.Module):
+    def __init__(self,
+                 block=BasicBlockERes2Net,
+                 block_fuse=BasicBlockERes2Net_diff_AFF,
+                 num_blocks=[3, 4, 6, 3],
+                 m_channels=32,
+                 feat_dim=80,
+                 embedding_size=192,
+                 pooling_func='TSTP',
+                 two_emb_layer=False):
+        super(ERes2Net, self).__init__()
+        self.in_planes = m_channels
+        self.feat_dim = feat_dim
+        self.embedding_size = embedding_size
+        self.stats_dim = int(feat_dim / 8) * m_channels * 8
+        self.two_emb_layer = two_emb_layer
+
+        self.conv1 = nn.Conv2d(1,
+                               m_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(m_channels)
+        self.layer1 = self._make_layer(block,
+                                       m_channels,
+                                       num_blocks[0],
+                                       stride=1)
+        self.layer2 = self._make_layer(block,
+                                       m_channels * 2,
+                                       num_blocks[1],
+                                       stride=2)
+        self.layer3 = self._make_layer(block_fuse,
+                                       m_channels * 4,
+                                       num_blocks[2],
+                                       stride=2)
+        self.layer4 = self._make_layer(block_fuse,
+                                       m_channels * 8,
+                                       num_blocks[3],
+                                       stride=2)
+
+        # Downsampling module for each layer
+        self.layer1_downsample = nn.Conv2d(m_channels * 2, m_channels * 4, kernel_size=3, stride=2, padding=1,
+                                           bias=False)
+        self.layer2_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.layer3_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+
+        # Bottom-up fusion module
+        self.fuse_mode12 = AFF(channels=m_channels * 4)
+        self.fuse_mode123 = AFF(channels=m_channels * 8)
+        self.fuse_mode1234 = AFF(channels=m_channels * 16)
+
+        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(
+            in_dim=self.stats_dim * block.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
+                               embedding_size)
+        if self.two_emb_layer:
+            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
+            self.seg_2 = nn.Linear(embedding_size, embedding_size)
+        else:
+            self.seg_bn_1 = nn.Identity()
+            self.seg_2 = nn.Identity()
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+        x = x.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(x)))
+        out1 = self.layer1(out)
+        out2 = self.layer2(out1)
+        out1_downsample = self.layer1_downsample(out1)
+        fuse_out12 = self.fuse_mode12(out2, out1_downsample)
+        out3 = self.layer3(out2)
+        fuse_out12_downsample = self.layer2_downsample(fuse_out12)
+        fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
+        out4 = self.layer4(out3)
+        fuse_out123_downsample = self.layer3_downsample(fuse_out123)
+        fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample)
+        stats = self.pool(fuse_out1234)
+
+        embed_a = self.seg_1(stats)
+        if self.two_emb_layer:
+            out = F.relu(embed_a)
+            out = self.seg_bn_1(out)
+            embed_b = self.seg_2(out)
+            return embed_b
+        else:
+            return embed_a
+
+
+class BasicBlockRes2Net(nn.Module):
+    expansion = 2
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=32, scale=2):
+        super(BasicBlockRes2Net, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale - 1
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes,
+                          self.expansion * planes,
+                          kernel_size=1,
+                          stride=stride,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = torch.cat((out, spx[self.nums]), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class Res2Net(nn.Module):
+    def __init__(self,
+                 block=BasicBlockRes2Net,
+                 num_blocks=[3, 4, 6, 3],
+                 m_channels=32,
+                 feat_dim=80,
+                 embedding_size=192,
+                 pooling_func='TSTP',
+                 two_emb_layer=False):
+        super(Res2Net, self).__init__()
+        self.in_planes = m_channels
+        self.feat_dim = feat_dim
+        self.embedding_size = embedding_size
+        self.stats_dim = int(feat_dim / 8) * m_channels * 8
+        self.two_emb_layer = two_emb_layer
+
+        self.conv1 = nn.Conv2d(1,
+                               m_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(m_channels)
+        self.layer1 = self._make_layer(block,
+                                       m_channels,
+                                       num_blocks[0],
+                                       stride=1)
+        self.layer2 = self._make_layer(block,
+                                       m_channels * 2,
+                                       num_blocks[1],
+                                       stride=2)
+        self.layer3 = self._make_layer(block,
+                                       m_channels * 4,
+                                       num_blocks[2],
+                                       stride=2)
+        self.layer4 = self._make_layer(block,
+                                       m_channels * 8,
+                                       num_blocks[3],
+                                       stride=2)
+
+        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(
+            in_dim=self.stats_dim * block.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
+                               embedding_size)
+        if self.two_emb_layer:
+            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
+            self.seg_2 = nn.Linear(embedding_size, embedding_size)
+        else:
+            self.seg_bn_1 = nn.Identity()
+            self.seg_2 = nn.Identity()
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+
+        x = x.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.layer1(out)
+        out = self.layer2(out)
+        out = self.layer3(out)
+        out = self.layer4(out)
+
+        stats = self.pool(out)
+
+        embed_a = self.seg_1(stats)
+        if self.two_emb_layer:
+            out = F.relu(embed_a)
+            out = self.seg_bn_1(out)
+            embed_b = self.seg_2(out)
+            return embed_b
+        else:
+            return embed_a
@@ -0,0 +1,273 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+""" Res2Net implementation is adapted from https://github.com/wenet-e2e/wespeaker.
+    ERes2Net incorporates both local and global feature fusion techniques to improve the performance. 
+    The local feature fusion (LFF) fuses the features within one single residual block to extract the local signal.
+    The global feature fusion (GFF) takes acoustic features of different scales as input to aggregate global signal.
+    ERes2Net-Large is an upgraded version of ERes2Net that uses a larger number of parameters to achieve better 
+    recognition performance. Parameters expansion, baseWidth, and scale can be modified to obtain optimal performance.
+"""
+
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+import funasr.models.pooling.pooling_layers as pooling_layers
+from funasr.modules.cnn.fusion import AFF
+
+
+class ReLU(nn.Hardtanh):
+
+    def __init__(self, inplace=False):
+        super(ReLU, self).__init__(0, 20, inplace)
+
+    def __repr__(self):
+        inplace_str = 'inplace' if self.inplace else ''
+        return self.__class__.__name__ + ' (' \
+            + inplace_str + ')'
+
+
+def conv1x1(in_planes, out_planes, stride=1):
+    "1x1 convolution without padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride,
+                     padding=0, bias=False)
+
+
+def conv3x3(in_planes, out_planes, stride=1):
+    "3x3 convolution with padding"
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
+                     padding=1, bias=False)
+
+
+class BasicBlockERes2Net(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
+        super(BasicBlockERes2Net, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+        self.nums = scale
+
+        convs = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes,
+                          self.expansion * planes,
+                          kernel_size=1,
+                          stride=stride,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = sp + spx[i]
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class BasicBlockERes2Net_diff_AFF(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_planes, planes, stride=1, baseWidth=24, scale=3):
+        super(BasicBlockERes2Net_diff_AFF, self).__init__()
+        width = int(math.floor(planes * (baseWidth / 64.0)))
+        self.conv1 = conv1x1(in_planes, width * scale, stride)
+        self.bn1 = nn.BatchNorm2d(width * scale)
+
+        self.nums = scale
+
+        convs = []
+        fuse_models = []
+        bns = []
+        for i in range(self.nums):
+            convs.append(conv3x3(width, width))
+            bns.append(nn.BatchNorm2d(width))
+        for j in range(self.nums - 1):
+            fuse_models.append(AFF(channels=width))
+
+        self.convs = nn.ModuleList(convs)
+        self.bns = nn.ModuleList(bns)
+        self.fuse_models = nn.ModuleList(fuse_models)
+        self.relu = ReLU(inplace=True)
+
+        self.conv3 = conv1x1(width * scale, planes * self.expansion)
+        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes,
+                          self.expansion * planes,
+                          kernel_size=1,
+                          stride=stride,
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+        self.stride = stride
+        self.width = width
+        self.scale = scale
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+        spx = torch.split(out, self.width, 1)
+        for i in range(self.nums):
+            if i == 0:
+                sp = spx[i]
+            else:
+                sp = self.fuse_models[i - 1](sp, spx[i])
+
+            sp = self.convs[i](sp)
+            sp = self.relu(self.bns[i](sp))
+            if i == 0:
+                out = sp
+            else:
+                out = torch.cat((out, sp), 1)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        residual = self.shortcut(x)
+        out += residual
+        out = self.relu(out)
+
+        return out
+
+
+class ERes2NetAug(nn.Module):
+    def __init__(self,
+                 block=BasicBlockERes2Net,
+                 block_fuse=BasicBlockERes2Net_diff_AFF,
+                 num_blocks=[3, 4, 6, 3],
+                 m_channels=64,
+                 feat_dim=80,
+                 embedding_size=192,
+                 pooling_func='TSTP',
+                 two_emb_layer=False):
+        super(ERes2NetAug, self).__init__()
+        self.in_planes = m_channels
+        self.feat_dim = feat_dim
+        self.embedding_size = embedding_size
+        self.stats_dim = int(feat_dim / 8) * m_channels * 8
+        self.two_emb_layer = two_emb_layer
+
+        self.conv1 = nn.Conv2d(1,
+                               m_channels,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(m_channels)
+        self.layer1 = self._make_layer(block,
+                                       m_channels,
+                                       num_blocks[0],
+                                       stride=1)
+        self.layer2 = self._make_layer(block,
+                                       m_channels * 2,
+                                       num_blocks[1],
+                                       stride=2)
+        self.layer3 = self._make_layer(block_fuse,
+                                       m_channels * 4,
+                                       num_blocks[2],
+                                       stride=2)
+        self.layer4 = self._make_layer(block_fuse,
+                                       m_channels * 8,
+                                       num_blocks[3],
+                                       stride=2)
+
+        self.layer1_downsample = nn.Conv2d(m_channels * 4, m_channels * 8, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.layer2_downsample = nn.Conv2d(m_channels * 8, m_channels * 16, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.layer3_downsample = nn.Conv2d(m_channels * 16, m_channels * 32, kernel_size=3, padding=1, stride=2,
+                                           bias=False)
+        self.fuse_mode12 = AFF(channels=m_channels * 8)
+        self.fuse_mode123 = AFF(channels=m_channels * 16)
+        self.fuse_mode1234 = AFF(channels=m_channels * 32)
+
+        self.n_stats = 1 if pooling_func == 'TAP' or pooling_func == "TSDP" else 2
+        self.pool = getattr(pooling_layers, pooling_func)(
+            in_dim=self.stats_dim * block.expansion)
+        self.seg_1 = nn.Linear(self.stats_dim * block.expansion * self.n_stats,
+                               embedding_size)
+        if self.two_emb_layer:
+            self.seg_bn_1 = nn.BatchNorm1d(embedding_size, affine=False)
+            self.seg_2 = nn.Linear(embedding_size, embedding_size)
+        else:
+            self.seg_bn_1 = nn.Identity()
+            self.seg_2 = nn.Identity()
+
+    def _make_layer(self, block, planes, num_blocks, stride):
+        strides = [stride] + [1] * (num_blocks - 1)
+        layers = []
+        for stride in strides:
+            layers.append(block(self.in_planes, planes, stride))
+            self.in_planes = planes * block.expansion
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
+
+        x = x.unsqueeze_(1)
+        out = F.relu(self.bn1(self.conv1(x)))
+        out1 = self.layer1(out)
+        out2 = self.layer2(out1)
+        out1_downsample = self.layer1_downsample(out1)
+        fuse_out12 = self.fuse_mode12(out2, out1_downsample)
+        out3 = self.layer3(out2)
+        fuse_out12_downsample = self.layer2_downsample(fuse_out12)
+        fuse_out123 = self.fuse_mode123(out3, fuse_out12_downsample)
+        out4 = self.layer4(out3)
+        fuse_out123_downsample = self.layer3_downsample(fuse_out123)
+        fuse_out1234 = self.fuse_mode1234(out4, fuse_out123_downsample)
+        stats = self.pool(fuse_out1234)
+
+        embed_a = self.seg_1(stats)
+        if self.two_emb_layer:
+            out = F.relu(embed_a)
+            out = self.seg_bn_1(out)
+            embed_b = self.seg_2(out)
+            return embed_b
+        else:
+            return embed_a
@@ -0,0 +1,3 @@
+from .DTDNN import CAMPPlus
+from .ResNet import ERes2Net
+from .ResNet_aug import ERes2NetAug
@@ -0,0 +1,29 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+import torch
+import torch.nn as nn
+
+
+class AFF(nn.Module):
+
+    def __init__(self, channels=64, r=4):
+        super(AFF, self).__init__()
+        inter_channels = int(channels // r)
+
+        self.local_att = nn.Sequential(
+            nn.Conv2d(channels * 2, inter_channels, kernel_size=1, stride=1, padding=0),
+            nn.BatchNorm2d(inter_channels),
+            nn.SiLU(inplace=True),
+            nn.Conv2d(inter_channels, channels, kernel_size=1, stride=1, padding=0),
+            nn.BatchNorm2d(channels),
+        )
+
+    def forward(self, x, ds_y):
+        xa = torch.cat((x, ds_y), dim=1)
+        x_att = self.local_att(xa)
+        x_att = 1.0 + torch.tanh(x_att)
+        xo = torch.mul(x, x_att) + torch.mul(ds_y, 2.0-x_att)
+
+        return xo
+
@@ -0,0 +1,254 @@
+# Copyright 3D-Speaker (https://github.com/alibaba-damo-academy/3D-Speaker). All Rights Reserved.
+# Licensed under the Apache License, Version 2.0 (http://www.apache.org/licenses/LICENSE-2.0)
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from torch import nn
+
+
+def get_nonlinear(config_str, channels):
+    nonlinear = nn.Sequential()
+    for name in config_str.split('-'):
+        if name == 'relu':
+            nonlinear.add_module('relu', nn.ReLU(inplace=True))
+        elif name == 'prelu':
+            nonlinear.add_module('prelu', nn.PReLU(channels))
+        elif name == 'batchnorm':
+            nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels))
+        elif name == 'batchnorm_':
+            nonlinear.add_module('batchnorm',
+                                 nn.BatchNorm1d(channels, affine=False))
+        else:
+            raise ValueError('Unexpected module ({}).'.format(name))
+    return nonlinear
+
+
+def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
+    mean = x.mean(dim=dim)
+    std = x.std(dim=dim, unbiased=unbiased)
+    stats = torch.cat([mean, std], dim=-1)
+    if keepdim:
+        stats = stats.unsqueeze(dim=dim)
+    return stats
+
+
+class StatsPool(nn.Module):
+    def forward(self, x):
+        return statistics_pooling(x)
+
+
+class TDNNLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 bias=False,
+                 config_str='batchnorm-relu'):
+        super(TDNNLayer, self).__init__()
+        if padding < 0:
+            assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
+                kernel_size)
+            padding = (kernel_size - 1) // 2 * dilation
+        self.linear = nn.Conv1d(in_channels,
+                                out_channels,
+                                kernel_size,
+                                stride=stride,
+                                padding=padding,
+                                dilation=dilation,
+                                bias=bias)
+        self.nonlinear = get_nonlinear(config_str, out_channels)
+
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.nonlinear(x)
+        return x
+
+
+class CAMLayer(nn.Module):
+    def __init__(self,
+                 bn_channels,
+                 out_channels,
+                 kernel_size,
+                 stride,
+                 padding,
+                 dilation,
+                 bias,
+                 reduction=2):
+        super(CAMLayer, self).__init__()
+        self.linear_local = nn.Conv1d(bn_channels,
+                                      out_channels,
+                                      kernel_size,
+                                      stride=stride,
+                                      padding=padding,
+                                      dilation=dilation,
+                                      bias=bias)
+        self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1)
+        self.relu = nn.ReLU(inplace=True)
+        self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        y = self.linear_local(x)
+        context = x.mean(-1, keepdim=True) + self.seg_pooling(x)
+        context = self.relu(self.linear1(context))
+        m = self.sigmoid(self.linear2(context))
+        return y * m
+
+    def seg_pooling(self, x, seg_len=100, stype='avg'):
+        if stype == 'avg':
+            seg = F.avg_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
+        elif stype == 'max':
+            seg = F.max_pool1d(x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
+        else:
+            raise ValueError('Wrong segment pooling type.')
+        shape = seg.shape
+        seg = seg.unsqueeze(-1).expand(*shape, seg_len).reshape(*shape[:-1], -1)
+        seg = seg[..., :x.shape[-1]]
+        return seg
+
+
+class CAMDenseTDNNLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 bn_channels,
+                 kernel_size,
+                 stride=1,
+                 dilation=1,
+                 bias=False,
+                 config_str='batchnorm-relu',
+                 memory_efficient=False):
+        super(CAMDenseTDNNLayer, self).__init__()
+        assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
+            kernel_size)
+        padding = (kernel_size - 1) // 2 * dilation
+        self.memory_efficient = memory_efficient
+        self.nonlinear1 = get_nonlinear(config_str, in_channels)
+        self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
+        self.nonlinear2 = get_nonlinear(config_str, bn_channels)
+        self.cam_layer = CAMLayer(bn_channels,
+                                  out_channels,
+                                  kernel_size,
+                                  stride=stride,
+                                  padding=padding,
+                                  dilation=dilation,
+                                  bias=bias)
+
+    def bn_function(self, x):
+        return self.linear1(self.nonlinear1(x))
+
+    def forward(self, x):
+        if self.training and self.memory_efficient:
+            x = cp.checkpoint(self.bn_function, x)
+        else:
+            x = self.bn_function(x)
+        x = self.cam_layer(self.nonlinear2(x))
+        return x
+
+
+class CAMDenseTDNNBlock(nn.ModuleList):
+    def __init__(self,
+                 num_layers,
+                 in_channels,
+                 out_channels,
+                 bn_channels,
+                 kernel_size,
+                 stride=1,
+                 dilation=1,
+                 bias=False,
+                 config_str='batchnorm-relu',
+                 memory_efficient=False):
+        super(CAMDenseTDNNBlock, self).__init__()
+        for i in range(num_layers):
+            layer = CAMDenseTDNNLayer(in_channels=in_channels + i * out_channels,
+                                      out_channels=out_channels,
+                                      bn_channels=bn_channels,
+                                      kernel_size=kernel_size,
+                                      stride=stride,
+                                      dilation=dilation,
+                                      bias=bias,
+                                      config_str=config_str,
+                                      memory_efficient=memory_efficient)
+            self.add_module('tdnnd%d' % (i + 1), layer)
+
+    def forward(self, x):
+        for layer in self:
+            x = torch.cat([x, layer(x)], dim=1)
+        return x
+
+
+class TransitLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 bias=True,
+                 config_str='batchnorm-relu'):
+        super(TransitLayer, self).__init__()
+        self.nonlinear = get_nonlinear(config_str, in_channels)
+        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+
+    def forward(self, x):
+        x = self.nonlinear(x)
+        x = self.linear(x)
+        return x
+
+
+class DenseLayer(nn.Module):
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 bias=False,
+                 config_str='batchnorm-relu'):
+        super(DenseLayer, self).__init__()
+        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
+        self.nonlinear = get_nonlinear(config_str, out_channels)
+
+    def forward(self, x):
+        if len(x.shape) == 2:
+            x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
+        else:
+            x = self.linear(x)
+        x = self.nonlinear(x)
+        return x
+
+
+class BasicResBlock(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_planes, planes, stride=1):
+        super(BasicResBlock, self).__init__()
+        self.conv1 = nn.Conv2d(in_planes,
+                               planes,
+                               kernel_size=3,
+                               stride=(stride, 1),
+                               padding=1,
+                               bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes,
+                               planes,
+                               kernel_size=3,
+                               stride=1,
+                               padding=1,
+                               bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+
+        self.shortcut = nn.Sequential()
+        if stride != 1 or in_planes != self.expansion * planes:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes,
+                          self.expansion * planes,
+                          kernel_size=1,
+                          stride=(stride, 1),
+                          bias=False),
+                nn.BatchNorm2d(self.expansion * planes))
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(x)
+        out = F.relu(out)
+        return out
@@ -2,23 +2,17 @@
 """ Some implementations are adapted from https://github.com/yuyq96/D-TDNN
 """

+import io
+from typing import Union
+
+import librosa as sf
+import numpy as np
 import torch
 import torch.nn.functional as F
-import torch.utils.checkpoint as cp
+import torchaudio.compliance.kaldi as Kaldi
 from torch import nn

-import io
-import os
-from typing import Any, Dict, List, Union
-
-import numpy as np
-import librosa as sf
-import torch
-import torchaudio
-import logging
 from funasr.utils.modelscope_file import File
-from collections import OrderedDict
-import torchaudio.compliance.kaldi as Kaldi


 def check_audio_list(audio: list):
@@ -103,230 +97,6 @@ def sv_chunk(vad_segments: list, fs = 16000) -> list:
    return segs


-class BasicResBlock(nn.Module):
-    expansion = 1
-
-    def __init__(self, in_planes, planes, stride=1):
-        super(BasicResBlock, self).__init__()
-        self.conv1 = nn.Conv2d(
-            in_planes,
-            planes,
-            kernel_size=3,
-            stride=(stride, 1),
-            padding=1,
-            bias=False)
-        self.bn1 = nn.BatchNorm2d(planes)
-        self.conv2 = nn.Conv2d(
-            planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn2 = nn.BatchNorm2d(planes)
-
-        self.shortcut = nn.Sequential()
-        if stride != 1 or in_planes != self.expansion * planes:
-            self.shortcut = nn.Sequential(
-                nn.Conv2d(
-                    in_planes,
-                    self.expansion * planes,
-                    kernel_size=1,
-                    stride=(stride, 1),
-                    bias=False), nn.BatchNorm2d(self.expansion * planes))
-
-    def forward(self, x):
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = self.bn2(self.conv2(out))
-        out += self.shortcut(x)
-        out = F.relu(out)
-        return out
-
-
-class FCM(nn.Module):
-
-    def __init__(self,
-                 block=BasicResBlock,
-                 num_blocks=[2, 2],
-                 m_channels=32,
-                 feat_dim=80):
-        super(FCM, self).__init__()
-        self.in_planes = m_channels
-        self.conv1 = nn.Conv2d(
-            1, m_channels, kernel_size=3, stride=1, padding=1, bias=False)
-        self.bn1 = nn.BatchNorm2d(m_channels)
-
-        self.layer1 = self._make_layer(
-            block, m_channels, num_blocks[0], stride=2)
-        self.layer2 = self._make_layer(
-            block, m_channels, num_blocks[0], stride=2)
-
-        self.conv2 = nn.Conv2d(
-            m_channels,
-            m_channels,
-            kernel_size=3,
-            stride=(2, 1),
-            padding=1,
-            bias=False)
-        self.bn2 = nn.BatchNorm2d(m_channels)
-        self.out_channels = m_channels * (feat_dim // 8)
-
-    def _make_layer(self, block, planes, num_blocks, stride):
-        strides = [stride] + [1] * (num_blocks - 1)
-        layers = []
-        for stride in strides:
-            layers.append(block(self.in_planes, planes, stride))
-            self.in_planes = planes * block.expansion
-        return nn.Sequential(*layers)
-
-    def forward(self, x):
-        x = x.unsqueeze(1)
-        out = F.relu(self.bn1(self.conv1(x)))
-        out = self.layer1(out)
-        out = self.layer2(out)
-        out = F.relu(self.bn2(self.conv2(out)))
-
-        shape = out.shape
-        out = out.reshape(shape[0], shape[1] * shape[2], shape[3])
-        return out
-
-
-class CAMPPlus(nn.Module):
-
-    def __init__(self,
-                 feat_dim=80,
-                 embedding_size=192,
-                 growth_rate=32,
-                 bn_size=4,
-                 init_channels=128,
-                 config_str='batchnorm-relu',
-                 memory_efficient=True,
-                 output_level='segment'):
-        super(CAMPPlus, self).__init__()
-
-        self.head = FCM(feat_dim=feat_dim)
-        channels = self.head.out_channels
-        self.output_level = output_level
-
-        self.xvector = nn.Sequential(
-            OrderedDict([
-                ('tdnn',
-                 TDNNLayer(
-                     channels,
-                     init_channels,
-                     5,
-                     stride=2,
-                     dilation=1,
-                     padding=-1,
-                     config_str=config_str)),
-            ]))
-        channels = init_channels
-        for i, (num_layers, kernel_size, dilation) in enumerate(
-                zip((12, 24, 16), (3, 3, 3), (1, 2, 2))):
-            block = CAMDenseTDNNBlock(
-                num_layers=num_layers,
-                in_channels=channels,
-                out_channels=growth_rate,
-                bn_channels=bn_size * growth_rate,
-                kernel_size=kernel_size,
-                dilation=dilation,
-                config_str=config_str,
-                memory_efficient=memory_efficient)
-            self.xvector.add_module('block%d' % (i + 1), block)
-            channels = channels + num_layers * growth_rate
-            self.xvector.add_module(
-                'transit%d' % (i + 1),
-                TransitLayer(
-                    channels, channels // 2, bias=False,
-                    config_str=config_str))
-            channels //= 2
-
-        self.xvector.add_module('out_nonlinear',
-                                get_nonlinear(config_str, channels))
-
-        if self.output_level == 'segment':
-            self.xvector.add_module('stats', StatsPool())
-            self.xvector.add_module(
-                'dense',
-                DenseLayer(
-                    channels * 2, embedding_size, config_str='batchnorm_'))
-        else:
-            assert self.output_level == 'frame', '`output_level` should be set to \'segment\' or \'frame\'. '
-
-        for m in self.modules():
-            if isinstance(m, (nn.Conv1d, nn.Linear)):
-                nn.init.kaiming_normal_(m.weight.data)
-                if m.bias is not None:
-                    nn.init.zeros_(m.bias)
-
-    def forward(self, x):
-        x = x.permute(0, 2, 1)  # (B,T,F) => (B,F,T)
-        x = self.head(x)
-        x = self.xvector(x)
-        if self.output_level == 'frame':
-            x = x.transpose(1, 2)
-        return x
-
-
-def get_nonlinear(config_str, channels):
-    nonlinear = nn.Sequential()
-    for name in config_str.split('-'):
-        if name == 'relu':
-            nonlinear.add_module('relu', nn.ReLU(inplace=True))
-        elif name == 'prelu':
-            nonlinear.add_module('prelu', nn.PReLU(channels))
-        elif name == 'batchnorm':
-            nonlinear.add_module('batchnorm', nn.BatchNorm1d(channels))
-        elif name == 'batchnorm_':
-            nonlinear.add_module('batchnorm',
-                                 nn.BatchNorm1d(channels, affine=False))
-        else:
-            raise ValueError('Unexpected module ({}).'.format(name))
-    return nonlinear
-
-
-def statistics_pooling(x, dim=-1, keepdim=False, unbiased=True, eps=1e-2):
-    mean = x.mean(dim=dim)
-    std = x.std(dim=dim, unbiased=unbiased)
-    stats = torch.cat([mean, std], dim=-1)
-    if keepdim:
-        stats = stats.unsqueeze(dim=dim)
-    return stats
-
-
-class StatsPool(nn.Module):
-
-    def forward(self, x):
-        return statistics_pooling(x)
-
-
-class TDNNLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 kernel_size,
-                 stride=1,
-                 padding=0,
-                 dilation=1,
-                 bias=False,
-                 config_str='batchnorm-relu'):
-        super(TDNNLayer, self).__init__()
-        if padding < 0:
-            assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
-                kernel_size)
-            padding = (kernel_size - 1) // 2 * dilation
-        self.linear = nn.Conv1d(
-            in_channels,
-            out_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            bias=bias)
-        self.nonlinear = get_nonlinear(config_str, out_channels)
-
-    def forward(self, x):
-        x = self.linear(x)
-        x = self.nonlinear(x)
-        return x
-
-
 def extract_feature(audio):
    features = []
    for au in audio:
@@ -338,164 +108,6 @@ def extract_feature(audio):
    return features


-class CAMLayer(nn.Module):
-
-    def __init__(self,
-                 bn_channels,
-                 out_channels,
-                 kernel_size,
-                 stride,
-                 padding,
-                 dilation,
-                 bias,
-                 reduction=2):
-        super(CAMLayer, self).__init__()
-        self.linear_local = nn.Conv1d(
-            bn_channels,
-            out_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            bias=bias)
-        self.linear1 = nn.Conv1d(bn_channels, bn_channels // reduction, 1)
-        self.relu = nn.ReLU(inplace=True)
-        self.linear2 = nn.Conv1d(bn_channels // reduction, out_channels, 1)
-        self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x):
-        y = self.linear_local(x)
-        context = x.mean(-1, keepdim=True) + self.seg_pooling(x)
-        context = self.relu(self.linear1(context))
-        m = self.sigmoid(self.linear2(context))
-        return y * m
-
-    def seg_pooling(self, x, seg_len=100, stype='avg'):
-        if stype == 'avg':
-            seg = F.avg_pool1d(
-                x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
-        elif stype == 'max':
-            seg = F.max_pool1d(
-                x, kernel_size=seg_len, stride=seg_len, ceil_mode=True)
-        else:
-            raise ValueError('Wrong segment pooling type.')
-        shape = seg.shape
-        seg = seg.unsqueeze(-1).expand(*shape,
-                                       seg_len).reshape(*shape[:-1], -1)
-        seg = seg[..., :x.shape[-1]]
-        return seg
-
-
-class CAMDenseTDNNLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 bn_channels,
-                 kernel_size,
-                 stride=1,
-                 dilation=1,
-                 bias=False,
-                 config_str='batchnorm-relu',
-                 memory_efficient=False):
-        super(CAMDenseTDNNLayer, self).__init__()
-        assert kernel_size % 2 == 1, 'Expect equal paddings, but got even kernel size ({})'.format(
-            kernel_size)
-        padding = (kernel_size - 1) // 2 * dilation
-        self.memory_efficient = memory_efficient
-        self.nonlinear1 = get_nonlinear(config_str, in_channels)
-        self.linear1 = nn.Conv1d(in_channels, bn_channels, 1, bias=False)
-        self.nonlinear2 = get_nonlinear(config_str, bn_channels)
-        self.cam_layer = CAMLayer(
-            bn_channels,
-            out_channels,
-            kernel_size,
-            stride=stride,
-            padding=padding,
-            dilation=dilation,
-            bias=bias)
-
-    def bn_function(self, x):
-        return self.linear1(self.nonlinear1(x))
-
-    def forward(self, x):
-        if self.training and self.memory_efficient:
-            x = cp.checkpoint(self.bn_function, x)
-        else:
-            x = self.bn_function(x)
-        x = self.cam_layer(self.nonlinear2(x))
-        return x
-
-
-class CAMDenseTDNNBlock(nn.ModuleList):
-
-    def __init__(self,
-                 num_layers,
-                 in_channels,
-                 out_channels,
-                 bn_channels,
-                 kernel_size,
-                 stride=1,
-                 dilation=1,
-                 bias=False,
-                 config_str='batchnorm-relu',
-                 memory_efficient=False):
-        super(CAMDenseTDNNBlock, self).__init__()
-        for i in range(num_layers):
-            layer = CAMDenseTDNNLayer(
-                in_channels=in_channels + i * out_channels,
-                out_channels=out_channels,
-                bn_channels=bn_channels,
-                kernel_size=kernel_size,
-                stride=stride,
-                dilation=dilation,
-                bias=bias,
-                config_str=config_str,
-                memory_efficient=memory_efficient)
-            self.add_module('tdnnd%d' % (i + 1), layer)
-
-    def forward(self, x):
-        for layer in self:
-            x = torch.cat([x, layer(x)], dim=1)
-        return x
-
-
-class TransitLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 bias=True,
-                 config_str='batchnorm-relu'):
-        super(TransitLayer, self).__init__()
-        self.nonlinear = get_nonlinear(config_str, in_channels)
-        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
-
-    def forward(self, x):
-        x = self.nonlinear(x)
-        x = self.linear(x)
-        return x
-
-
-class DenseLayer(nn.Module):
-
-    def __init__(self,
-                 in_channels,
-                 out_channels,
-                 bias=False,
-                 config_str='batchnorm-relu'):
-        super(DenseLayer, self).__init__()
-        self.linear = nn.Conv1d(in_channels, out_channels, 1, bias=bias)
-        self.nonlinear = get_nonlinear(config_str, out_channels)
-
-    def forward(self, x):
-        if len(x.shape) == 2:
-            x = self.linear(x.unsqueeze(dim=-1)).squeeze(dim=-1)
-        else:
-            x = self.linear(x)
-        x = self.nonlinear(x)
-        return x
-
 def postprocess(segments: list, vad_segments: list,
                labels: np.ndarray, embeddings: np.ndarray) -> list:
    assert len(segments) == len(labels)
@@ -590,4 +202,4 @@ def distribute_spk(sentence_list, sd_time_list):
                sentence_spk = spk
        d['spk'] = sentence_spk
        sd_sentence_list.append(d)
-    return sd_sentence_list
+    return sd_sentence_list
@@ -1 +1 @@
-0.8.6
+0.8.7
@@ -47,7 +47,7 @@ public class MainActivity extends AppCompatActivity {
    public static final int CHUNK_INTERVAL = 10;
    public static final int SEND_SIZE = 1920;
    // 热词
-    private String hotWords = "阿里巴巴 达摩院 夜雨飘零";
+    private String hotWords = "阿里巴巴 20\n达摩院 20\n夜雨飘零 20\n";
    // 采样率
    public static final int SAMPLE_RATE = 16000;
    // 声道数
@@ -287,6 +287,7 @@ public class MainActivity extends AppCompatActivity {
            }
        });
        String message = getMessage(true);
+        Log.d(TAG, "WebSocket发送消息: " + message);
        webSocket.send(message);

        audioRecord.startRecording();
@@ -319,7 +320,23 @@ public class MainActivity extends AppCompatActivity {
            obj.put("chunk_interval", CHUNK_INTERVAL);
            obj.put("wav_name", "default");
            if (!hotWords.equals("")) {
-                obj.put("hotwords", hotWords);
+                JSONObject hotwordsJSON = new JSONObject();
+                // 分割每一行字符串
+                String[] hotWordsList = hotWords.split("\n");
+                for (String s : hotWordsList) {
+                    if (s.equals("")) {
+                        Log.w(TAG, "hotWords为空");
+                        continue;
+                    }
+                    // 按照空格分割字符串
+                    String[] hotWordsArray = s.split(" ");
+                    if (hotWordsArray.length != 2) {
+                        Log.w(TAG, "hotWords格式不正确");
+                        continue;
+                    }
+                    hotwordsJSON.put(hotWordsArray[0], Integer.valueOf(hotWordsArray[1]));
+                }
+                obj.put("hotwords", hotwordsJSON.toString());
            }
            obj.put("wav_format", "pcm");
            obj.put("is_speaking", isSpeaking);
@@ -13,6 +13,6 @@
        android:background="@drawable/edittext_border"
        android:minLines="3"
        android:gravity="top"
-        android:hint="每个热词用空格隔开" />
+        android:hint="每一行为:热词 权重" />

 </LinearLayout>
@@ -75,9 +75,6 @@ nohup bash run_server.sh \
  --punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
  --itn-dir thuduj12/fst_itn_zh \
  --lm-dir damo/speech_ngram_lm_zh-cn-ai-wesp-fst \
-  --decoder-thread-num 32 \
-  --io-thread-num  8 \
-  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key \
  --hotword ../../hotwords.txt > log.out 2>&1 &
@@ -94,10 +91,11 @@ Introduction to run_server.sh parameters:
 --punc-quant: True for quantized PUNC model, False for non-quantized PUNC model. Default is True.
 --itn-dir modelscope model ID or local model path.
 --port: Port number that the server listens on. Default is 10095.
--decoder-thread-num: The number of thread pools on the server side that can handle concurrent requests. The default value is 8.
+--decoder-thread-num: The number of thread pools on the server side that can handle concurrent requests.
+                      The script will automatically configure parameters decoder-thread-num and io-thread-num based on the server's thread count.
+--io-thread-num: Number of IO threads that the server starts.
 --model-thread-num: The number of internal threads for each recognition route to control the parallelism of the ONNX model. 
        The default value is 1. It is recommended that decoder-thread-num * model-thread-num equals the total number of threads.
--io-thread-num: Number of IO threads that the server starts. Default is 1.
 --certfile <string>: SSL certificate file. Default is ../../../ssl_key/server.crt. If you want to close ssl，set 0
 --keyfile <string>: SSL key file. Default is ../../../ssl_key/server.key. 
 --hotword: Hotword file path, one line for each hotword(e.g.:阿里巴巴 20), if the client provides hot words, then combined with the hot words provided by the client.
@@ -55,9 +55,6 @@ nohup bash run_server.sh \
  --model-dir damo/speech_paraformer-large_asr_nat-en-16k-common-vocab10020-onnx \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
-  --decoder-thread-num 32 \
-  --io-thread-num  8 \
-  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key > log.out 2>&1 &
 ```
@@ -73,10 +70,11 @@ Introduction to run_server.sh parameters:
 --punc-quant: True for quantized PUNC model, False for non-quantized PUNC model. Default is True.
 --itn-dir modelscope model ID or local model path.
 --port: Port number that the server listens on. Default is 10095.
--decoder-thread-num: The number of thread pools on the server side that can handle concurrent requests. The default value is 8.
+--decoder-thread-num: The number of thread pools on the server side that can handle concurrent requests.
+                      The script will automatically configure parameters decoder-thread-num and io-thread-num based on the server's thread count.
+--io-thread-num: Number of IO threads that the server starts.
 --model-thread-num: The number of internal threads for each recognition route to control the parallelism of the ONNX model. 
        The default value is 1. It is recommended that decoder-thread-num * model-thread-num equals the total number of threads.
--io-thread-num: Number of IO threads that the server starts. Default is 1.
 --certfile <string>: SSL certificate file. Default is ../../../ssl_key/server.crt. If you want to close ssl，set 0
 --keyfile <string>: SSL key file. Default is ../../../ssl_key/server.key. 
 ```
@@ -141,9 +141,6 @@ nohup bash run_server.sh \
  --model-dir damo/speech_paraformer-large_asr_nat-en-16k-common-vocab10020-onnx \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
-  --decoder-thread-num 32 \
-  --io-thread-num  8 \
-  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key > log.out 2>&1 &
 ```
@@ -158,10 +155,11 @@ nohup bash run_server.sh \
 --punc-quant   True为量化PUNC模型，False为非量化PUNC模型，默认是True
 --itn-dir modelscope model ID 或者 本地模型路径
 --port  服务端监听的端口号，默认为 10095
--decoder-thread-num  服务端线程池个数(支持的最大并发路数)，默认为 8
+--decoder-thread-num  服务端线程池个数(支持的最大并发路数)，
+                      脚本会根据服务器线程数自动配置decoder-thread-num、io-thread-num
+--io-thread-num  服务端启动的IO线程数
 --model-thread-num  每路识别的内部线程数(控制ONNX模型的并行)，默认为 1，
                    其中建议 decoder-thread-num*model-thread-num 等于总线程数
--io-thread-num  服务端启动的IO线程数，默认为 1
 --certfile  ssl的证书文件，默认为：../../../ssl_key/server.crt，如果需要关闭ssl，参数设置为0
 --keyfile   ssl的密钥文件，默认为：../../../ssl_key/server.key
 ```
@@ -155,10 +155,8 @@ nohup bash run_server.sh \
  --model-dir damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-onnx \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --punc-dir damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx \
+  --lm-dir damo/speech_ngram_lm_zh-cn-ai-wesp-fst \
  --itn-dir thuduj12/fst_itn_zh \
-  --decoder-thread-num 32 \
-  --io-thread-num  8 \
-  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key \
  --hotword ../../hotwords.txt  > log.out 2>&1 &
@@ -175,10 +173,11 @@ nohup bash run_server.sh \
 --lm-dir modelscope model ID 或者 本地模型路径
 --itn-dir modelscope model ID 或者 本地模型路径
 --port  服务端监听的端口号，默认为 10095
--decoder-thread-num  服务端线程池个数(支持的最大并发路数)，默认为 8
+--decoder-thread-num  服务端线程池个数(支持的最大并发路数)，
+                      脚本会根据服务器线程数自动配置decoder-thread-num、io-thread-num
+--io-thread-num  服务端启动的IO线程数
 --model-thread-num  每路识别的内部线程数(控制ONNX模型的并行)，默认为 1，
                    其中建议 decoder-thread-num*model-thread-num 等于总线程数
--io-thread-num  服务端启动的IO线程数，默认为 1
 --certfile  ssl的证书文件，默认为：../../../ssl_key/server.crt，如果需要关闭ssl，参数设置为0
 --keyfile   ssl的密钥文件，默认为：../../../ssl_key/server.key
 --hotword   热词文件路径，每行一个热词，格式：热词 权重(例如:阿里巴巴 20)，
@@ -85,9 +85,6 @@ nohup bash run_server_2pass.sh \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --punc-dir damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727-onnx \
  --itn-dir thuduj12/fst_itn_zh \
-  --decoder-thread-num 32 \
-  --io-thread-num  8 \
-  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key \
  --hotword ../../hotwords.txt > log.out 2>&1 &
@@ -111,10 +108,11 @@ nohup bash run_server_2pass.sh \
 --punc-quant: True for quantized PUNC model, False for non-quantized PUNC model. Default is True.
 --itn-dir modelscope model ID or local model path.
 --port: Port number that the server listens on. Default is 10095.
--decoder-thread-num: The number of thread pools on the server side that can handle concurrent requests. The default value is 8.
+--decoder-thread-num: The number of thread pools on the server side that can handle concurrent requests.
+                      The script will automatically configure parameters decoder-thread-num and io-thread-num based on the server's thread count.
+--io-thread-num: Number of IO threads that the server starts.
 --model-thread-num: The number of internal threads for each recognition route to control the parallelism of the ONNX model. 
        The default value is 1. It is recommended that decoder-thread-num * model-thread-num equals the total number of threads.
--io-thread-num: Number of IO threads that the server starts. Default is 1.
 --certfile <string>: SSL certificate file. Default is ../../../ssl_key/server.crt. If you want to close ssl，set 0
 --keyfile <string>: SSL key file. Default is ../../../ssl_key/server.key. 
 --hotword: Hotword file path, one line for each hotword(e.g.:阿里巴巴 20), if the client provides hot words, then combined with the hot words provided by the client.
@@ -101,9 +101,6 @@ nohup bash run_server_2pass.sh \
  --vad-dir damo/speech_fsmn_vad_zh-cn-16k-common-onnx \
  --punc-dir damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727-onnx \
  --itn-dir thuduj12/fst_itn_zh \
-  --decoder-thread-num 32 \
-  --io-thread-num  8 \
-  --port 10095 \
  --certfile  ../../../ssl_key/server.crt \
  --keyfile ../../../ssl_key/server.key \
  --hotword ../../hotwords.txt > log.out 2>&1 &
@@ -120,10 +117,11 @@ nohup bash run_server_2pass.sh \
 --punc-quant   True为量化PUNC模型，False为非量化PUNC模型，默认是True
 --itn-dir modelscope model ID 或者 本地模型路径
 --port  服务端监听的端口号，默认为 10095
--decoder-thread-num  服务端线程池个数(支持的最大并发路数)，默认为 8
+--decoder-thread-num  服务端线程池个数(支持的最大并发路数)，
+                      脚本会根据服务器线程数自动配置decoder-thread-num、io-thread-num
+--io-thread-num  服务端启动的IO线程数
 --model-thread-num  每路识别的内部线程数(控制ONNX模型的并行)，默认为 1，
                    其中建议 decoder-thread-num*model-thread-num 等于总线程数
--io-thread-num  服务端启动的IO线程数，默认为 1
 --certfile  ssl的证书文件，默认为：../../../ssl_key/server.crt，如果需要关闭ssl，参数设置为0
 --keyfile   ssl的密钥文件，默认为：../../../ssl_key/server.key
 --hotword   热词文件路径，每行一个热词，格式：热词 权重(例如:阿里巴巴 20)，
@@ -48,8 +48,13 @@ option(HAVE_PYTHON "Build python" OFF)
 option(HAVE_SPECIAL "Build special" ON)

  set(WITH_GFLAGS OFF CACHE BOOL "whether build glog with gflags" FORCE)
-  set(HAVE_BIN ON CACHE BOOL "Build the fst binaries" FORCE)
-  set(HAVE_SCRIPT ON CACHE BOOL "Build the fstscript" FORCE)
+  if (WIN32)
+    set(HAVE_BIN OFF CACHE BOOL "Build the fst binaries" FORCE)
+    set(HAVE_SCRIPT OFF CACHE BOOL "Build the fstscript" FORCE)
+  else()
+    set(HAVE_BIN ON CACHE BOOL "Build the fst binaries" FORCE)
+    set(HAVE_SCRIPT ON CACHE BOOL "Build the fstscript" FORCE)
+  endif (WIN32)

  set(HAVE_COMPACT OFF CACHE BOOL "Build compact" FORCE)
  set(HAVE_CONST OFF CACHE BOOL "Build const" FORCE)
@@ -5,33 +5,24 @@ vad_dir="damo/speech_fsmn_vad_zh-cn-16k-common-onnx"
 punc_dir="damo/punc_ct-transformer_cn-en-common-vocab471067-large-onnx"
 itn_dir="thuduj12/fst_itn_zh"
 lm_dir="damo/speech_ngram_lm_zh-cn-ai-wesp-fst"
-decoder_thread_num=32
-model_thread_num=1
-io_thread_num=8
 port=10095
 certfile="../../../ssl_key/server.crt"
 keyfile="../../../ssl_key/server.key"
 hotword="../../hotwords.txt"
+# set decoder_thread_num
+decoder_thread_num=$(cat /proc/cpuinfo | grep "processor"|wc -l) || { echo "Get cpuinfo failed. Set decoder_thread_num = 32"; decoder_thread_num=32; }
+multiple_io=16
+io_thread_num=$(( (decoder_thread_num + multiple_io - 1) / multiple_io ))
+model_thread_num=1

 . ../egs/aishell/transformer/utils/parse_options.sh || exit 1;

+if [ -z "$certfile" ] || [ "$certfile" = "0" ]; then
+  certfile=""
+  keyfile=""
+fi
+
 cd /workspace/FunASR/runtime/websocket/build/bin
-if [ -z "$certfile" ] || [ "$certfile" -eq 0 ]; then
-./funasr-wss-server  \
-  --download-model-dir "${download_model_dir}" \
-  --model-dir "${model_dir}" \
-  --vad-dir "${vad_dir}" \
-  --punc-dir "${punc_dir}" \
-  --itn-dir "${itn_dir}" \
-  --lm-dir "${lm_dir}" \
-  --decoder-thread-num ${decoder_thread_num} \
-  --io-thread-num  ${io_thread_num} \
-  --model-thread-num ${model_thread_num} \
-  --port ${port} \
-  --certfile  "" \
-  --keyfile "" \
-  --hotword "${hotword}"
-else
 ./funasr-wss-server  \
  --download-model-dir "${download_model_dir}" \
  --model-dir "${model_dir}" \
@@ -46,4 +37,4 @@ else
  --certfile  "${certfile}" \
  --keyfile "${keyfile}" \
  --hotword "${hotword}"
-fi
+
@@ -5,33 +5,24 @@ online_model_dir="damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab840
 vad_dir="damo/speech_fsmn_vad_zh-cn-16k-common-onnx"
 punc_dir="damo/punc_ct-transformer_zh-cn-common-vad_realtime-vocab272727-onnx"
 itn_dir="thuduj12/fst_itn_zh"
-decoder_thread_num=32
-model_thread_num=1
-io_thread_num=8
 port=10095
 certfile="../../../ssl_key/server.crt"
 keyfile="../../../ssl_key/server.key"
 hotword="../../hotwords.txt"
+# set decoder_thread_num
+decoder_thread_num=$(cat /proc/cpuinfo | grep "processor"|wc -l) || { echo "Get cpuinfo failed. Set decoder_thread_num = 32"; decoder_thread_num=32; }
+multiple_io=16
+io_thread_num=$(( (decoder_thread_num + multiple_io - 1) / multiple_io ))
+model_thread_num=1

 . ../egs/aishell/transformer/utils/parse_options.sh || exit 1;

+if [ -z "$certfile" ] || [ "$certfile" = "0" ]; then
+  certfile=""
+  keyfile=""
+fi
+
 cd /workspace/FunASR/runtime/websocket/build/bin
-if [ -z "$certfile" ] || [ "$certfile" -eq 0 ]; then
-./funasr-wss-server-2pass  \
-  --download-model-dir "${download_model_dir}" \
-  --model-dir "${model_dir}" \
-  --online-model-dir "${online_model_dir}" \
-  --vad-dir "${vad_dir}" \
-  --punc-dir "${punc_dir}" \
-  --itn-dir "${itn_dir}" \
-  --decoder-thread-num ${decoder_thread_num} \
-  --model-thread-num ${model_thread_num} \
-  --io-thread-num  ${io_thread_num} \
-  --port ${port} \
-  --certfile  "" \
-  --keyfile "" \
-  --hotword "${hotword}"
-else
 ./funasr-wss-server-2pass  \
  --download-model-dir "${download_model_dir}" \
  --model-dir "${model_dir}" \
@@ -46,4 +37,4 @@ else
  --certfile  "${certfile}" \
  --keyfile "${keyfile}" \
  --hotword "${hotword}"
-fi
+
@@ -10,6 +10,12 @@ option(ENABLE_WEBSOCKET "Whether to build websocket server" ON)
 option(ENABLE_PORTAUDIO "Whether to build portaudio" ON)

 if(WIN32)
+  file(REMOVE ${PROJECT_SOURCE_DIR}/../onnxruntime/third_party/glog/src/config.h 
+    ${PROJECT_SOURCE_DIR}/../onnxruntime/third_party/glog/src/glog/export.h 
+    ${PROJECT_SOURCE_DIR}/../onnxruntime/third_party/glog/src/glog/logging.h 
+    ${PROJECT_SOURCE_DIR}/../onnxruntime/third_party/glog/src/glog/raw_logging.h 
+    ${PROJECT_SOURCE_DIR}/../onnxruntime/third_party/glog/src/glog/stl_logging.h 
+    ${PROJECT_SOURCE_DIR}/../onnxruntime/third_party/glog/src/glog/vlog_is_on.h)
 else()
  set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -pthread -fPIC")
 endif()
@@ -28,8 +28,11 @@ int main(int argc, char* argv[]) {
  try {
    google::InitGoogleLogging(argv[0]);
    FLAGS_logtostderr = true;
-
-    TCLAP::CmdLine cmd("funasr-wss-server", ' ', "1.0");
+    std::string tpass_version = "";
+#ifdef _WIN32
+    tpass_version = "0.1.0";
+#endif
+    TCLAP::CmdLine cmd("funasr-wss-server", ' ', tpass_version);
    TCLAP::ValueArg<std::string> download_model_dir(
        "", "download-model-dir",
        "Download model from Modelscope to download_model_dir", false,
@@ -96,11 +99,11 @@ int main(int argc, char* argv[]) {
                                           "0.0.0.0", "string");
    TCLAP::ValueArg<int> port("", "port", "port", false, 10095, "int");
    TCLAP::ValueArg<int> io_thread_num("", "io-thread-num", "io thread num",
-                                       false, 8, "int");
+                                       false, 2, "int");
    TCLAP::ValueArg<int> decoder_thread_num(
        "", "decoder-thread-num", "decoder thread num", false, 8, "int");
    TCLAP::ValueArg<int> model_thread_num("", "model-thread-num",
-                                          "model thread num", false, 4, "int");
+                                          "model thread num", false, 2, "int");

    TCLAP::ValueArg<std::string> certfile(
        "", "certfile",
@@ -231,6 +234,16 @@ int main(int argc, char* argv[]) {
        std::string down_asr_path;
        std::string down_asr_model;

+        size_t found = s_offline_asr_path.find("speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404");
+        if (found != std::string::npos) {
+            model_path["offline-model-revision"]="v1.2.4";
+        } else{
+            found = s_offline_asr_path.find("speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404");
+            if (found != std::string::npos) {
+                model_path["offline-model-revision"]="v1.0.5";
+            }
+        }
+
        if (access(s_offline_asr_path.c_str(), F_OK) == 0) {
          // local
          python_cmd_asr = python_cmd + " --model-name " + s_offline_asr_path +
@@ -239,16 +252,6 @@ int main(int argc, char* argv[]) {
          down_asr_path = s_offline_asr_path;
        } 
        else {
-          size_t found = s_offline_asr_path.find("speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404");
-          if (found != std::string::npos) {
-              model_path["offline-model-revision"]="v1.2.4";
-          } else{
-              found = s_offline_asr_path.find("speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404");
-              if (found != std::string::npos) {
-                  model_path["offline-model-revision"]="v1.0.5";
-              }
-          }
-
          // modelscope
          LOG(INFO) << "Download model: " << s_offline_asr_path
                    << " from modelscope : "; 
@@ -30,8 +30,11 @@ int main(int argc, char* argv[]) {

    google::InitGoogleLogging(argv[0]);
    FLAGS_logtostderr = true;
-
-    TCLAP::CmdLine cmd("funasr-wss-server", ' ', "1.0");
+    std::string offline_version = "";
+#ifdef _WIN32
+    offline_version = "0.1.0";
+#endif
+    TCLAP::CmdLine cmd("funasr-wss-server", ' ', offline_version);
    TCLAP::ValueArg<std::string> download_model_dir(
        "", "download-model-dir",
        "Download model from Modelscope to download_model_dir",
@@ -88,7 +91,7 @@ int main(int argc, char* argv[]) {
                                           "0.0.0.0", "string");
    TCLAP::ValueArg<int> port("", "port", "port", false, 10095, "int");
    TCLAP::ValueArg<int> io_thread_num("", "io-thread-num", "io thread num",
-                                       false, 8, "int");
+                                       false, 2, "int");
    TCLAP::ValueArg<int> decoder_thread_num(
        "", "decoder-thread-num", "decoder thread num", false, 8, "int");
    TCLAP::ValueArg<int> model_thread_num("", "model-thread-num",
@@ -223,28 +226,29 @@ int main(int argc, char* argv[]) {
            std::string down_asr_path;
            std::string down_asr_model;

+            // modify model-revision by model name
+            size_t found = s_asr_path.find("speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404");
+            if (found != std::string::npos) {
+                model_path["model-revision"]="v1.2.4";
+            }
+
+            found = s_asr_path.find("speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404");
+            if (found != std::string::npos) {
+                model_path["model-revision"]="v1.0.5";
+            }
+
+            found = s_asr_path.find("speech_paraformer-large_asr_nat-en-16k-common-vocab10020");
+            if (found != std::string::npos) {
+                model_path["model-revision"]="v1.0.0";
+                s_itn_path="";
+                s_lm_path="";
+            }
+
            if (access(s_asr_path.c_str(), F_OK) == 0){
                // local
                python_cmd_asr = python_cmd + " --model-name " + s_asr_path + " --export-dir ./ " + " --model_revision " + model_path["model-revision"];
                down_asr_path  = s_asr_path;
            }else{
-                size_t found = s_asr_path.find("speech_paraformer-large-vad-punc_asr_nat-zh-cn-16k-common-vocab8404");
-                if (found != std::string::npos) {
-                    model_path["model-revision"]="v1.2.4";
-                }
-
-                found = s_asr_path.find("speech_paraformer-large-contextual_asr_nat-zh-cn-16k-common-vocab8404");
-                if (found != std::string::npos) {
-                    model_path["model-revision"]="v1.0.5";
-                }
-
-                found = s_asr_path.find("speech_paraformer-large_asr_nat-en-16k-common-vocab10020");
-                if (found != std::string::npos) {
-                    model_path["model-revision"]="v1.0.0";
-                    s_itn_path="";
-                    s_lm_path="";
-                }
-
                // modelscope
                LOG(INFO) << "Download model: " <<  s_asr_path << " from modelscope: ";
                python_cmd_asr = python_cmd + " --model-name " + s_asr_path + " --export-dir " + s_download_model_dir + " --model_revision " + model_path["model-revision"];
@@ -98,8 +98,8 @@ void WebSocketServer::do_decoder(
    std::string wav_format,
    FUNASR_HANDLE& tpass_online_handle) {
  // lock for each connection
-  scoped_lock guard(thread_lock);
  if(!tpass_online_handle){
+    scoped_lock guard(thread_lock);
 	  LOG(INFO) << "tpass_online_handle  is free, return";
 	  msg["access_num"]=(int)msg["access_num"]-1;
 	  return;
@@ -128,10 +128,12 @@ void WebSocketServer::do_decoder(
                                       hotwords_embedding, itn);

        } else {
+          scoped_lock guard(thread_lock);
          msg["access_num"]=(int)msg["access_num"]-1;
          return;
        }
      } catch (std::exception const& e) {
+        scoped_lock guard(thread_lock);
        LOG(ERROR) << e.what();
        msg["access_num"]=(int)msg["access_num"]-1;
        return;
@@ -162,10 +164,12 @@ void WebSocketServer::do_decoder(
                                       wav_format, (ASR_TYPE)asr_mode_,
                                       hotwords_embedding, itn);
        } else {
+          scoped_lock guard(thread_lock);
          msg["access_num"]=(int)msg["access_num"]-1;	 
          return;
        }
      } catch (std::exception const& e) {
+        scoped_lock guard(thread_lock);
        LOG(ERROR) << e.what();
        msg["access_num"]=(int)msg["access_num"]-1;
        return;
@@ -209,6 +213,7 @@ void WebSocketServer::do_decoder(
  } catch (std::exception const& e) {
    std::cerr << "Error: " << e.what() << std::endl;
  }
+  scoped_lock guard(thread_lock);
  msg["access_num"]=(int)msg["access_num"]-1;
 
 }
@@ -68,7 +68,6 @@ void WebSocketServer::do_decoder(const std::vector<char>& buffer,
                                 int audio_fs,
                                 std::string wav_format,
                                 FUNASR_DEC_HANDLE& decoder_handle) {
-  scoped_lock guard(thread_lock);
  try {
    int num_samples = buffer.size();  // the size of the buf

@@ -130,6 +129,7 @@ void WebSocketServer::do_decoder(const std::vector<char>& buffer,
  } catch (std::exception const& e) {
    std::cerr << "Error: " << e.what() << std::endl;
  }
+  scoped_lock guard(thread_lock);
  msg["access_num"]=(int)msg["access_num"]-1;
 }

@@ -19,7 +19,7 @@ requirements = {
        # "soundfile>=0.12.1",
        # "h5py>=3.1.0",
        "kaldiio>=2.17.0",
-        # "torch_complex",
+        "torch_complex",
        # "nltk>=3.4.5",
        # ASR
        "sentencepiece", # train
@@ -30,7 +30,7 @@ requirements = {
        # "pypinyin>=0.44.0",
        # "espnet_tts_frontend",
        # ENH
-        # "pytorch_wpe",
+        "pytorch_wpe",
        "editdistance>=0.5.2",
        "tensorboard",
        # "g2p",
@@ -43,6 +43,7 @@ requirements = {
        "tqdm",
        "hdbscan",
        "umap",
+        "jaconv",
    ],
    # train: The modules invoked when training only.
    "train": [
@@ -1 +1 @@
 .8.6
 .8.7